Efficient and quick extraction of unknown objects in cluttered 3D scenes plays a significant role in robotics tasks such as object search, grasping, and manipulation. This paper describes a geometric-based unsupervised approach for the segmentation of cluttered scenes into objects. The proposed method first over-segments the raw point clouds into supervoxels to provide a more natural representation of 3D point clouds and reduce the computational cost with a minimal loss of geometric information. Then the fully connected local area linkage graph is used to distinguish between planar and nonplanar adjacent patches. Then the initial segmentation is completed utilizing the geometric features and local surface convexities. After the initial segmentation, many subgraphs are generated, each of which represents an individual object or part of it. Finally, we use the plane extracted from the scene to refine the initial segmentation result under the framework of global energy optimization. Experiments on the Object Cluttered Indoor Dataset dataset indicate that the proposed method can outperform the representative segmentation algorithms in terms of weighted overlap and accuracy, while our method has good robustness and real-time performance.

中文翻译：

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使用具有几何特征的能量优化的高效点云分割方法，用于3D场景理解

在杂乱的3D场景中高效，快速地提取未知对象在诸如对象搜索，抓取和操纵之类的机器人任务中起着重要作用。本文介绍了一种基于几何的无监督方法，用于将杂乱的场景分割为对象。所提出的方法首先将原始点云过度分割为超体素，以提供更自然的3D点云表示，并以最小的几何信息损失降低计算成本。然后，使用完全连接的局部连锁图来区分平面和非平面的相邻面片。然后，利用几何特征和局部表面凸度完成初始分割。初始分割后，将生成许多子图，每个子图代表一个单独的对象或对象的一部分。最后，在全局能量优化的框架下，我们使用从场景中提取的平面来细化初始分割结果。在室内杂物数据集数据集上进行的实验表明，该方法在加权重叠和准确度方面都优于典型的分割算法，而该方法具有良好的鲁棒性和实时性。